Machine learning based scatter correction

Abstract

The present approach relates to the use of machine-learning in convolution kernel design for scatter correction. In one aspect, a neural network is trained to replace or improve the convolution kernel used for scatter correction. The training data set may be generated probabilistically so that actual measurements are not employed.

Description

TECHNICAL FIELD[0001]The subject matter disclosed herein relates to scatter correction in imaging contexts, and in particular to the use of machine learning techniques to facilitate scatter artifact correction or reduction.BACKGROUND[0002]Non-invasive imaging technologies allow images of the internal structures or features of a patient / object to be obtained without performing an invasive procedure on the patient / object. In particular, such non-invasive imaging technologies rely on various physical principles (such as the differential transmission of X-rays through a target volume, the reflection of acoustic waves within the volume, the paramagnetic properties of different tissues and materials within the volume, the breakdown of targeted radionuclides within the body, and so forth) to acquire data and to construct images or otherwise represent the observed internal features of the patient / object.[0003]Certain such imaging techniques, including Positrons Emission Tomography (PET), X-...

AI Technical Summary

Benefits of technology

[0007]For computed tomography (CT), cone-beam CT (CBCT) systems, and / or other X-ray based systems, the convolution kernel based deconvolution method is a popular scatter correction algorithm that estimates the scatter profile directly from the projection data by convolving with spatially invariant kernel models. This method does not require additional hardware or scans, and the computational cost is typically low, especially compared to high-cost Monte-Carlo-based scatter simulation. However, the accuracy of the deconvolution method depends on the kernel design and the associated parameters. These parameters are usually determined empirically or based on a complicated optimization process and they may vary from one scan settings to another. For example, any change in the tube spectrum or in the pre-patient beam collimation will affect these parameters. Therefore, it is a challenge to design a kernel and determine the kernel parameters to accurately estimate the scatter profile.

Problems solved by technology

This method does not require additional hardware or scans, and the computational cost is typically low, especially compared to high-cost Monte-Carlo-based scatter simulation.

Therefore, it is a challenge to design a kernel and determine the kernel parameters to accurately estimate the scatter profile.

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